id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-120498-b1bla3fp	McFate, Clifton	SKATE: A Natural Language Interface for Encoding Structured Knowledge	2020-10-20		.txt	text/plain	3794	199	54	In this paper we describe how our approach, called SKATE, uses a neural semantic parser to parse NL input and suggest semi-structured templates, which are recursively filled to produce fully structured interpretations. We demonstrate how SKATE has been integrated with a natural language rule generation model to interactively acquire structured rules for story understanding, and conclude with a current application that uses SKATE to build COVID-19 policy diagrams. For example, in the second pane of Figure 2 , the template generator has built frame assignment options for the word "take." The resulting micro-dialogue is presented to the user. SKATE's performance improves with annotated examples, but they are not required, and as discussed in the next subsection, SKATE can generate its own training data as a new frame is selected by the user and elaborated upon in SKATE interactions. Our approach leverages recent advances in language modeling to generate templates from user text and to provide unstructured guidance.	./cache/cord-120498-b1bla3fp.txt	./txt/cord-120498-b1bla3fp.txt